Classification techniques are widely used for machine intelligence. Such techniques can be used for a wide variety of applications, such as, but not limited to, to perform image recognition, to control buying and selling of securities, to perform natural language processing, to perform medical detection functions (such as detecting cancer in an image), to perform stock market analysis (which can then be presented on a computer display, for example), to perform weather prediction, to perform analysis of motion in video images (which can then be used to generate new video (for display on a display, for example), to alert a user (e.g., in a security application), etc.), to provide bio sensors, to control systems (such as the motion of robots, the operation of HVAC systems, the operation of cruise control systems, the operation of navigation systems, etc.), etc.
Techniques for boosting the performance of classifiers are known. For example, the Adaboost technique can be used to boost the performance of weak classifiers making up a classifier by repeatedly training the classifier to provide updates to the classifier weights corresponding to the weak classifiers. Adaboost is described in Robert E. Schapire et al., “Boosting the Margin: a New Explanation for the Effectiveness of Voting Methods,” Annals of Statistics, 26(5):1651-1686, 1998, which is hereby incorporated by reference herein in its entirety.
In many instances, classifiers need to be updated when online—for example, when being used to perform classification. This the case, for example, because many classifiers are designed for general applications but only used in particular applications, where a more application-specific training would produce improved results. However, known techniques for boosting classifiers are inadequate.